Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images

Mohd Saad, Norhashimah and Azman, Izzatul Husna and Abdullah, Abdul Rahim and Hamzah, Rostam Affendi and Muda, Ahmad Sobri and Yamba, Farzanah Atikah (2025) Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images. International Journal of Advanced Technology and Engineering Exploration, 12 (129). pp. 1246-1263. ISSN 2394-7454

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Abstract

Stroke remains one of the leading causes of disability and mortality worldwide, necessitating timely and accurate diagnosis to improve treatment outcomes. This study presents a computer-aided diagnosis (CAD) system designed to detect and classify stroke lesions in magnetic resonance imaging (MRI), specifically utilizing diffusion-weighted imaging (DWI) sequences. A hybrid segmentation technique, fuzzy c-means with active contour (FCMAC), is proposed to enhance lesion localization accuracy. For classification, the system evaluates traditional machine learning algorithms like support vector machine (SVM) and k-nearest neighbor (KNN), alongside deep learning models such as convolutional neural network (CNN) and bilayered neural network (BNN). The entire diagnostic pipeline is integrated into a MATLAB-based graphical user interface (GUI), facilitating real-time analysis and ease of use in clinical settings. Experimental results show that the proposed FCMAC method achieves a dice coefficient (DC) of 0.654, outperforming conventional segmentation techniques. Among the classifiers, KNN offered the best balance between prediction accuracy and computational efficiency. The final system, termed SmartStroke-Pro, enables early detection and classification of stroke, providing a reliable and practical tool to assist healthcare professionals, particularly in resource-limited environments. This framework has the potential to reduce diagnostic delays and support improved clinical decision-making in acute stroke care.

Item Type:	Article
Uncontrolled Keywords:	Stroke diagnosis, Computer-aided diagnosis (CAD), DWI, Machine learning, Fuzzy c-means with active contour (FCMAC), SmartStroke-pro system.
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	23 Feb 2026 04:44
Last Modified:	23 Feb 2026 04:44
URI:	http://eprints.utem.edu.my/id/eprint/29556
Statistic Details:	View Download Statistic

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